Cross-platform execution in both fixed and floating point are supported. It includes a framework for easy handling of training data sets. It is easy to use, versatile, well documented, and fast. PHP, Python, Delphi and Mathematica bindings are available.

Here are some key features of "Fast Artificial Neural Network Library":
Multilayer Artificial Neural Network Library in C
Backpropagation training (RPROP, Quickprop, Batch, Incremental)
Evolving topology training which dynamically builds and trains the ANN (Cascade2)
Easy to use (create, train and run an ANN with just three function calls)
Fast (up to 150 times faster execution than other libraries)
Versatile (possible to adjust many parameters and features on-the-fly)
Well documented (An easy to use reference manual, a 50+ page university report describing the implementation considerations etc. and an introduction article)
Cross-platform (configure script for linux and unix, dll files for windows, project files for MSVC++ and Borland compilers are also reported to work)
Several different activation functions implemented (including stepwise linear functions for that extra bit of speed)
Easy to save and load entire ANNs
Several easy to use examples (simple train example and simple test example)
Can use both floating point and fixed point numbers (actually both float, double and int are available)
Cache optimized (for that extra bit of speed)
Open source (licenced under LGPL)
Framework for easy handling of training data sets
Graphical Interface
C++ Bindings
PHP Extension
Python Bindings
Delphi Bindings
.NET Bindings
Mathematica Extension
Octave Extension
Ruby Bindings
Pure Data Bindings
Debian package